Authors:
Francisco Benjamim Filho
;
Raul Pierre Renteria
and
Ruy Luiz Milidiú
Affiliation:
Pontifícia Universidade Católica do Rio de Janeiro, Brazil
Keyword(s):
Search engines, Keyword-based ranking, Link- based ranking.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Symbolic Systems
Abstract:
The WWW is a huge and rich environment. Web pages can be viewed as a large community of elements that are connected through links due to several issues. The HITS approach introduces two basic concepts, hubs and authorities, that reveal some hidden semantic information from the links. In this paper, we present XHITS, a generalization of HITS, that models multiple classes problems and a machine learning algorithm to calibrate it. We split classification influence into two sources. The first one is due to link propagation, whereas the second one is due to classification reinforcement. We derive a simple linear iterative equation to compute the classification values. We also provide an influence equation that shows how the two influence sources can be combined. Two special cases are explored: symmetric reinforcement and positive reinforcement. We show that for these two special cases the iterative scheme converges. Some illustrative examples and empirical test are also provided. They ind
icate that XHITS is a powerful and efficient modeling approach.
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